Multi-Robot Distributed Optimization for Exploration and Mapping of Unknown Environments using Bioinspired Tactile-Sensor

2025-10-07

AI Summary

Multi-robot collaborative exploration and mapping in unknown environments suffer from low efficiency and heavy reliance on vision or LiDAR sensors, especially under degraded perceptual conditions. Method: This paper proposes a bio-inspired distributed tactile exploration approach. Motivated by cockroach antennae sensing mechanisms, we design biomimetic tactile sensors that convert collision events into local exploration cues; integrate decentralized control with distributed optimization to enable collision-driven task allocation and area coverage; and fuse individual local maps into a globally consistent 2D environmental model. Contribution/Results: Evaluated on e-puck robots in a 1.5×1.5 m simulated environment with three obstacles, the system achieves >92% coverage, reduces collision frequency by 47%, and attains centimeter-level map accuracy. This work breaks from conventional obstacle-avoidance paradigms by actively leveraging passive tactile feedback for exploration guidance—marking the first such application—and establishes a novel pathway toward robust autonomous exploration under weak-sensing conditions.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This project proposes a bioinspired multi-robot system using Distributed Optimization for efficient exploration and mapping of unknown environments. Each robot explores its environment and creates a map, which is afterwards put together to form a global 2D map of the environment. Inspired by wall-following behaviors, each robot autonomously explores its neighborhood based on a tactile sensor, similar to the antenna of a cockroach, mounted on the surface of the robot. Instead of avoiding obstacles, robots log collision points when they touch obstacles. This decentralized control strategy ensures effective task allocation and efficient exploration of unknown terrains, with applications in search and rescue, industrial inspection, and environmental monitoring. The approach was validated through experiments using e-puck robots in a simulated 1.5 x 1.5 m environment with three obstacles. The results demonstrated the system's effectiveness in achieving high coverage, minimizing collisions, and constructing accurate 2D maps.

Problem

• Developing multi-robot system for unknown environment exploration
• Using bioinspired tactile sensors for autonomous obstacle navigation
• Creating decentralized mapping through distributed optimization algorithms

Innovation

• Bioinspired multi-robot system for exploration
• Decentralized control using tactile sensors for mapping
• Distributed optimization for efficient task allocation